Papers by Peng Cui
Single-Agent Generation Surpasses Multi-Agent Systems in Semantic Diversity (2026.findings-acl)
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| Challenge: | Multi-Agent Systems (MAS) are used to improve reasoning diversity and robustness by simulating interactions among agents with distinct roles. |
| Approach: | They find that a Multi-Output strategy produces the highest diversity without degrading logical validity. |
| Outcome: | The proposed approach outperforms multi-agent systems in semantic diversity . the results point to a more efficient and effective way to expand diversity - the authors say . |
EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing (2026.findings-acl)
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| Challenge: | Existing approaches to modifying large language models require continual updates to rectify outdated or erroneous knowledge. |
| Approach: | They propose a model editing strategy that mitigates catastrophic interference through sequential null-space alignment. |
| Outcome: | EvoEdit achieves better or comparable performance than prior state-of-the-art techniques with up to 3.53 speedup. |
ExpertIVS: Sociological Expert Driven Individual Value Simulation in Large Language Models (2026.findings-acl)
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Zhen Wang, Yuqi Ren, Yuehan Cui, Hongxiang Wang, Jianxiang Peng, Zhaoxia Zhang, Bingkun Zhu, Tongxuan Zhang, Dezhi Tong, Deyi Xiong
| Challenge: | Existing methods for social simulations mechanically stitch survey responses into prompts, which suffer from semantic fragmentation, failing to capture the internal coherence of human value systems. |
| Approach: | They propose a framework employing 14 Sociological Expert Agents to interpret World Values Survey responses through structured professional perspectives rather than direct responses concatenation. |
| Outcome: | Experiments on 480 individuals from 12 countries show that ExpertIVS outperforms baselines in value generalization and significantly outperfies the existing methods. |
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (2020.coling-main)
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| Challenge: | Existing extractive summarization models hardly capture inter-sentence relationships, especially in long documents. |
| Approach: | They propose to use a graph neural network to capture inter-sentence relationships efficiently via graph-structured document representation. |
| Outcome: | The proposed model outperforms existing models on CNN/DM and NYT datasets and significantly outperfies them on longer documents. |
Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) often experience “contextual hallucination” where they prioritize self-generated content over input context, leading to a disregard for pertinent details. |
| Approach: | They propose a method that dynamically adjusts attention maps to enhance contextual relevance by using a trained classifier to identify attention maps likely to induce hallucinations. |
| Outcome: | The proposed approach reduces hallucinations across open-source models on summarization and open-book QA tasks. |
Perception, Understanding and Reasoning: A Multimodal Benchmark for Video Fake News Detection (2026.acl-long)
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| Challenge: | Existing video fake news detection benchmarks focus on the detection accuracy, while failing to provide fine-grained assessments for the entire detection process. |
| Approach: | They propose a process-oriented video fake news detection benchmark that evaluates MLLMs' perception, understanding, and reasoning capabilities in VFND. |
| Outcome: | The proposed model achieves sota performance on video fake news detection tasks. |
CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2025.findings-emnlp)
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Feiyang Li, Peng Fang, Zhan Shi, Arijit Khan, Fang Wang, Weihao Wang, null Zhangxin-hw, Cui Yongjian
| Challenge: | Chain-of-thought reasoning has two key limitations: lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference logic. |
| Approach: | They propose a chain-of-thought reasoning framework with three key designs to address these issues. |
| Outcome: | The proposed framework improves the performance of large language models on complex tasks by incorporating knowledge graphs and learnable knowledge case-aware RAG. |
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)
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| Challenge: | Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers. |
| Approach: | They propose to unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM. |
| Outcome: | The proposed model outperforms Video-ChatGPT on image benchmarks and on 9 image benchmark benchmarks. |
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction (2023.findings-acl)
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| Challenge: | Existing methods for multi-modal relation extraction lack useful visual information. |
| Approach: | They propose a novel multi-modal relation extraction framework to capture deeper correlations of text, entity pair, and image/objects. |
| Outcome: | The proposed framework captures the deeper correlations of text, entity pair, and image/objects, and extracts useful information. |
How to Engage your Readers? Generating Guiding Questions to Promote Active Reading (2024.acl-long)
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| Challenge: | Using questions in written text is an effective strategy to enhance readability, but what makes an active reading question good, what the linguistic role of these questions is, and what is their impact on human reading remains understudied. |
| Approach: | They present a dataset of 10K in-text questions from textbooks and scientific articles and explore various approaches to generate such questions using language models. |
| Outcome: | The generated questions are of high quality and are almost as effective as human-written questions in terms of improving readers’ memorization and comprehension. |
Topic-Guided Abstractive Multi-Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing studies on multi-document summarization (MDS) focus on extractive and abstractive approaches to create a fluent and concise summary for a collection of thematically related documents. |
| Approach: | They propose a novel abstractive MDS model that represents multiple documents as a heterogeneous graph and then applies a graph-to-sequence framework to generate summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models on Rouge scores and human evaluation, while learning high-quality topics. |
Grammar Control in Dialogue Response Generation for Language Learning Chatbots (2025.naacl-long)
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| Challenge: | Existing language learning chatbots and research on second language acquisition benefit from these affordances. |
| Approach: | They ground a dialogue response generation model in a pedagogical repository of grammar skills and evaluate prompting, fine-tuning, and decoding strategies for grammar-controlled dialogue response generators. |
| Outcome: | The proposed model outperforms GPT-3.5 when tolerating minor response quality losses and predicts grammar-controlled responses to support grammar acquisition adapted to learner proficiency. |
Qwen2.5-xCoder: Multi-Agent Collaboration for Multilingual Code Instruction Tuning (2025.acl-long)
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Jian Yang, Wei Zhang, Yibo Miao, Shanghaoran Quan, Zhenhe Wu, Qiyao Peng, Liqun Yang, Tianyu Liu, Zeyu Cui, Binyuan Hui, Junyang Lin
| Challenge: | Existing methods to train code LLMs view each programming language in isolation . experimental results show that Qwen2.5-xCoder can bridge the gap between different programming languages . |
| Approach: | They propose a framework that allows agents to collaborate to enhance multilingual instruction tuning for code LLMs. |
| Outcome: | Experimental results show that Qwen2.5-xCoder can transfer knowledge efficiently and effectively between languages. |
CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory (2025.acl-long)
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Weichen Zhang, Chen Gao, Shiquan Yu, Ruiying Peng, Baining Zhao, Qian Zhang, Jinqiang Cui, Xinlei Chen, Yong Li
| Challenge: | Existing ground VLN agents struggle in aerial VLLN due to the lack of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration. |
| Approach: | They propose a large language model-empowered aerial VLN agent that decomposes the long-horizon task into sub-goals with different semantic levels. |
| Outcome: | The proposed method achieves state-of-the-art performance with significant improvement in continuous city environments. |
Sliding Selector Network with Dynamic Memory for Extractive Summarization of Long Documents (2021.naacl-main)
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| Challenge: | Existing summarization models suffer from the length limitation of text encoder, which results in huge loss of summary-relevant contents. |
| Approach: | They propose a sliding selector network with dynamic memory for extractive summarization of long-form documents that employs a window to extract summary sentences segment by segment. |
| Outcome: | The proposed model outperforms state-of-the-art models on two large-scale datasets showing that it is highly efficient and fluent. |
MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers (2026.acl-long)
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Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui
| Challenge: | Existing approaches to improve efficiency often enforce rigid structural constraints such as local attention windows. |
| Approach: | They propose a framework that augments sparse-attention mechanisms with dynamically integrated in-context information through an efficient retrieval system. |
| Outcome: | Empirical results show that MATCH significantly improves the performance of sparse-attention models on synthetic and real-world natural-language tasks. |
Adaptive and Personalized Exercise Generation for Online Language Learning (2023.acl-long)
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| Challenge: | Empirical studies have shown various benefits of adaptive learning, such as improved student learning outcomes, lower dropout rates, and increased instructor satisfaction. |
| Approach: | They propose to combine a knowledge tracing model that estimates each student’s evolving knowledge states from their learning history with a controlled text generation model that generates exercise sentences based on the student’ s current estimated knowledge state and instructor requirements of desired properties. |
| Outcome: | The proposed model can generate superior exercises based on student state and instructor requirements . Empirical studies have shown that adaptive learning improves student learning outcomes, lower dropout rates, and increased instructor satisfaction. |
DASR: Distributed Adaptive Scene Recognition - A Multi-Agent Cloud-Edge Framework for Language-Guided Scene Detection (2025.emnlp-industry)
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| Challenge: | Current approaches to analyzing driving scenarios rely on massive data collection followed by manual filtering. |
| Approach: | They propose a cloud-based framework for language-guided scene detection in connected vehicles . the framework leverages cloud- and edge-deployed large language models to identify relevant driving scenarios while optimizing on-vehicle buffer storage. |
| Outcome: | The proposed framework performs better on complex driving tasks and reduces storage requirements. |
Investigating the Zone of Proximal Development of Language Models for In-Context Learning (2025.findings-naacl)
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| Challenge: | In-context learning is a dynamic and progressive process where learners integrate new information into their knowledge base through interactions with the environment. |
| Approach: | They propose a learning analytics framework to analyze the in-context learning behavior of large language models (LLMs) through the lens of the Zone of Proximal Development (ZPD), an established theory in educational psychology. |
| Outcome: | The proposed framework improves inference and fine-tuning scenarios by selectively applying it to queries that are most likely to benefit from demonstrations. |